Fast Computation of Zigzag Persistence
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Abstract (arXiv)
Zigzag persistence is a powerful extension of the standard persistence which allows deletions of simplices besides insertions. However, computing zigzag persistence usually takes considerably more time than the standard persistence. We propose an algorithm called FastZigzag which narrows this efficiency gap. Our main result is that an input simplex-wise zigzag filtration can be converted to a cell-wise non-zigzag filtration of a Delta-complex with the same length, where the cells are copies of the input simplices. This conversion step in FastZigzag incurs very little cost. Furthermore, the barcode of the original filtration can be easily read from the barcode of the new cell-wise filtration because the conversion embodies a series of diamond switches known in topological data analysis. This seemingly simple observation opens up the vast possibilities for improving the computation of zigzag persistence because any efficient algorithm/software for standard persistence can now be applied to computing zigzag persistence. Our experiment shows that this indeed achieves substantial performance gain over the existing state-of-the-art softwares.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 1; 2026: 1.
6 papers.
- 2023GRIL: A 2-parameter Persistence Based Vectorization for Machine Learningcited by 13
- 2024Persistent Topological Features in Large Language Modelscited by 11
- 2024D-GRIL: End-to-End Topological Learning with 2-parameter Persistencecited by 5
- 2025Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Datacited by 1
- 2023Algebraic and Geometric Models for Space Networkingcited by 0
- 2026A differentiability framework for zigzag persistent homology via linear interpolationcited by 0
- 2026A differentiability framework for zigzag persistent homology via linear interpolation30 Sep 2026
- 2025Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data22 Feb 2025
- 2024Persistent Topological Features in Large Language Models14 Oct 2024
- 2024D-GRIL: End-to-End Topological Learning with 2-parameter Persistence11 Jun 2024
- 2023GRIL: A 2-parameter Persistence Based Vectorization for Machine Learning11 Apr 2023
- 2023Algebraic and Geometric Models for Space Networking3 Apr 2023